Data Science at LinkedIn: My Team

Lots of people ask me what it’s like to be a data scientist at LinkedIn. The short answer: it’s awesome. Folks like Pete Skomoroch and team are building data products related to identity and reputation, such as Skills and InMaps. Yael Garten is leading the effort to understand and increase mobile engagement. And other folks work on everything from open-source infrastructure to fraud detection. Amazing people helping our 160M+ members by deriving valuable insights from big data.

I wanted to take a moment to showcase my own team. As a team, we straddle the boundary between science and engineering. We work closely with several engineering teams to deliver products that our members use everyday.

Joseph Adler is a name you might recognize from your bookshelf: he wrote Baseball Hacks and R in a Nutshell, both published by O’Reilly. At LinkedIn, he is a data hacker extraordinaire, currently focused on improving the network update stream.

Ahmet Bugdayci just joined LinkedIn this year, and he’s already on a tear. He’s working on a better approach to representing job titles, one of the most fundamental facets of our members’ professional identity. And he’s a polyglot.

Heyning Cheng is our innovator in chief. He envisions data products and does whatever it takes to hack them together. Our recruiters are especially happy to be his beta testers, and we’re working to turn those prototypes into shipped product.

Gloria Lau leads all things data for the student initiative. Check out LinkedIn Alumni to see what she’s been up to. Students are the future, and we’re excited to be making LinkedIn a great tools for students, alumni, and universities.

Monica Rogati spearheaded many of LinkedIn’s key products: the Talent Match system that matches jobs to candidates; the first machine learning model for People You May Know; and the first version of Groups You May Like. When she’s not working on our products, she gives awesome presentations.

Daria Sorokina recently joined us and is working on search quality. She’s a hard-core machine learning researcher and developer: check out her open-source code for additive groves.

Ramesh Subramonian has been focused on data efforts for our international expansion. Over 60% of our members live outside the United States, and his efforts ensure that LinkedIn’s value proposition is a global one.

Joyce Wang is a data science generalist. She is part of the search team, but she’s built great tools for log analysis and human evaluation that are finding great use across the company.

I hope that gives you a flavor of what it’s like to be a data scientist at LinkedIn — and on my team in particular.

Do you possess that rare combination of computer science background, technical skill, creative problem-solving ability, and product sense? If so, then I’d love to talk with you about opportunities to work on challenging problems with amazing people!